基于混沌“微变异”自适应遗传算法研究
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陆军炮兵防空兵学院

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TP520

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Research on Adaptive Genetic Algorithm based on Chaos
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PLA Army Academy of Artillery and Air Defense

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    摘要:

    遗传算法可以较好地解决复杂的组合优化问题,但也存在两方面不足,一是搜索效率比其它优化算法低,二是容易过早收敛,陷入局部最优。针对这些问题,本文提出一种混沌“微变异”遗传算法。利用混沌优化算法具有随机性和遍历性的特点,解决了遗传算法容易陷入局部最优解的早熟问题,使得新算法同时具有较强的局部搜索能力和能够完成全局寻找最优解的能力。同时,对遗传算法的选择算子增加了混沌扰动,交叉算子和变异算子进行了自适应调整,对适应度函数进行改进,使遗传算法整体性能得到提高。最后,通过经典函数验证,混沌“微变异”遗传算法要比一般的混沌遗传算法和经典遗传算法的进化速度更快,搜索精度更高。

    Abstract:

    Complex combinatorial optimization problems can be solved by genetic algorithm, but there are also two shortcomings, one is the search efficiency is lower than other optimization algorithms, the other is easy to premature convergence and fall into local optimum. To solve these problems, the chaos "micro variation" genetic algorithm is proposed in this paper. The chaos optimization algorithm has the characteristics of randomness and ergodicity, which solves the premature problem that genetic algorithm is easy to fall into the local optimal solution, and makes the new algorithm have strong local search ability and the ability to complete the global search for the optimal solution. At the same time, the selection operator of genetic algorithm is added chaos disturbance, the crossover operator and mutation operator are adjusted adaptively, and the fitness function is improved. Based on the above, the overall performance of genetic algorithm is improved. Finally, the chaotic "micro mutation" genetic algorithm has faster evolution speed and higher search accuracy than the general chaotic genetic algorithm and classical genetic algorithm through the classical function verification.

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  • 收稿日期:2021-02-25
  • 最后修改日期:2021-03-29
  • 录用日期:2021-04-07
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